Xingjian Fu and Hongmei Guo
GBF-CMAC neural network, robust adaptive fault-tolerant control, low-altitude quadrotor UAV, attitude control
For the problem that the attitude of the low-altitude UAV is susceptible to the external environment disturbance and actuator failures, a composite control strategy is designed to combine the Gaussian basis function cerebellar model articulation controller (GBF-CMAC) neural network with robust adaptive fault-tolerant control (FTC). The symbolic distance is introduced to reduce the input dimension, and a GBF-CMAC neural network based on symbolic distance is proposed. The low-altitude UAV system model with failure and disturbances is established. The robust adaptive fault-tolerant controller design and conditions for asymptotic stability are given for the UAV system. The composite control strategy is provided by combining the GBF-CMAC neural networks with robust adaptive FTC. Finally, the semi-physical simulation of the composite control strategy is applied to the attitude system of the quadrotor UAV. The results verify the effectiveness of the composite control strategy.
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